Functional quantizer design for source localization in sensor networks

In this paper, we address the problem of quantizer design optimized for a source localization application in acoustic sensor networks where physically separated sensors make measurements of acoustic signal energy, quantize them, and transmit the quantized data to a fusion node, which then produces an estimate of the source location. We propose an iterative regular quantizer design algorithm that minimizes the localization error. To construct regular quantization partitions, we suggest the average distance error as a metric in the functional quantization since the distance is monotonic in each sensor reading. Furthermore, to guarantee minimization of the localization error, we propose a new technique to update the codewords and prove that the localization error can be reduced at each iteration while the average distance error remains nonincreasing by applying our update technique. Our experiments show that our proposed algorithm yields significantly improved performance as compared with traditional quantizer designs.